A sliced IGW distance is introduced with closed-form 1D expressions, rotational invariance, and studied structural and computational properties for efficient data alignment.
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GGDA framework generates knowledge-preserving intermediate graphs via FGW metric and a vertex-based progression to enable gradual domain adaptation across large graph distribution shifts.
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Sliced Inner Product Gromov-Wasserstein Distances
A sliced IGW distance is introduced with closed-form 1D expressions, rotational invariance, and studied structural and computational properties for efficient data alignment.
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Gradual Domain Adaptation for Graph Learning
GGDA framework generates knowledge-preserving intermediate graphs via FGW metric and a vertex-based progression to enable gradual domain adaptation across large graph distribution shifts.